Sentences with phrase «bivariate analyses»

Yet, disorders tend to co-occur, and when comorbidity is not taken into account, pairwise associations may simply represent indirect effects rather than direct associations.36, 37 For example, bivariate analyses may suggest that childhood anxiety disorders predict adolescent depression, but this association could be accounted for by comorbidity between childhood anxiety and depression.

All variables included in the bivariate analyses were included in the multivariate analyses.

The effect sizes derived from multivariate models with different control variables are therefore not comparable with each other and also not comparable with effect sizes derived from bivariate analyses.

Because bivariate analyses indicated that baseline levels of social support were highly correlated to levels at follow - up, in examining program effects on social support we controlled for their baseline levels by entering them first into the regression models.

The presence of fathers at home and regular contact with fathers was found to have little to no effect on these well - being outcome measures in the bivariate analyses.»

In bivariate analyses, intervention parents had more - favorable practices with regard to encouraging their child to read and using appropriate car seat restraints.

Data analyses involved bivariate analyses, logistic regression analyses and Cox proportional hazards regression analyses.

Bivariate analyses (considering one adversity at a time) and multivariate analyses (considering all adversities simultaneously) were conducted.

In bivariate analyses, we compared the distribution of quality - of - care outcomes and parenting practices using χ2 statistics for categorical variables and t statistics for continuous variables (eg, CBCL scores).

In bivariate analyses, we compared the distribution of parenting practices by using χ2 statistics for categorical variables.

As with our findings, there was evidence of significant prediction from adolescent to adult depression in bivariate analyses.

In bivariate analyses, we used 2 - way analysis of variance and χ2 tests of association to examine relationships between exposures, outcomes, and potential confounders.

The factors that we found associated with the 4 primary to secondary response groups must be considered as exploratory, since they are bivariate analyses, and do not control for other independent variables.

Bivariate analyses of the relation between maternal IPV and covariates (Table 1) were performed using Pearson χ2 tests to test differences in proportions.

An examination of collinearity was undertaken comparing changes in the standard errors and magnitude and sign (positive or negative) of the bivariate analyses results with the standard bivariate regression models for each sex and the full hierarchical regression models.

The overall procedure is as follows: First, baseline BMI SDS, age, gender of child / adolescent, parental education level, and familial (parents, siblings) obesity were introduced into the regression equation (when these data were significantly associated with the criterion in the bivariate analyses).

Specific statistical areas of expertise include factor and cluster analysis, basic bivariate analyses, repeated measures analyses, linear and hierarchical / mixed models, structural equation modeling, and nonparametric analyses including logistic regression techniques.

Bivariate analyses showed a significant difference between the groups regarding breastfeeding initiation rate (79 vs. 66 vs. 63 %, respectively; P < 0.05) for the intervention, attention control, and usual care groups.

A logistic regression analysis was conducted to adjust for the effects of variables identified through the bivariate analysis to be associated with either type of feeding or the presence of infection or sepsis / meningitis.

Associations between potential confounding variables and the presence of infection and sepsis / meningitis were also identified through this bivariate analysis.

In bivariate analysis, those who screened positive were more likely to have started tanning at an earlier age, be concerned about their appearance, and have depressive symptoms.

Bivariate analysis of factors associated with the 4 primary to secondary response groups showed that the poorer families use corporal punishment as both a primary and a secondary response (Table 5).

The results of the bivariate analysis for registration - linked and cross-sectional data indicated that the registration - linked data results are stable and representative compared with cross-sectional data.

Table 3 also shows (indicated by a superscript b) the independent variables that were not significantly related to the ACE total score in cross-sectional or linked bivariate analysis.

Among ideators, bivariate analysis revealed a significant relationship between physical abuse and suicidal ideation.

Findings from multivariate analysis, therefore, confirm findings of bivariate analysis for all groups, except for the ideators.

Bivariate analysis of relationships between pretreatment psychopathology and working alliance scores revealed no significant pattern of relationships but showed a tendency of an inverse relationship.

Some relationships that were non-significant in the bivariate analysis were significant in the path analysis.

Bivariate analysis of disordered eating characteristics in adolescence and young adulthood

Parent participation in the intervention was significantly associated with less Sexual Risk Behavior at 18 - months in the bivariate analysis.

Although this finding was not significant in the multivariate analysis, we did notice a trend in the bivariate analysis toward a decline in adequate well - child care rates with increasing depressive symptoms.

Although the overall trend in the bivariate analysis showed a decline in breastfeeding rates with increasing depressive symptoms, this finding was not statistically significant.

Results Adolescent depression significantly predicted young adult depression in the bivariate analysis, but this effect was entirely accounted for by comorbidity of adolescent depression with adolescent oppositional defiant disorder, anxiety, and substance disorders in adjusted analyses.

Not exact matches

Weighted bivariate and multivariate analyses of parenting behaviors were performed while controlling for demographics and paternal substance abuse.

National sampling weights were applied in all descriptive, bivariate, and multivariate analyses to yield nationally representative results.

After examining the unadjusted, bivariate associations with delayed OL, we used logistic regression analysis to estimate the adjusted odds ratio (OR) and 95 % CI in multiple variable models.

The bivariate approach is the foundation of if - then analysis, highlighting the link between the cause and the effect variables and any real correlation.

To investigate the relationship between learning styles, teacher demographics, and teacher retention, bivariate logistic regression analyses were conducted, and interactions between variables were tested for their predictive relationship with the binary outcome (retention).

Studying bike lanes in 90 or the 100 largest American cities, Pucher and collaborater Ralph Buehl used Pearson's correlation, bivariate quartile analysis, and two different types of regressions to measure the relationship between more and longer bike lanes and quantity of cyclists.

Zero - order correlation analyses were conducted to test bivariate associations between the predictor and criterion variables.

Finally, in case of redundant predictors (in comparison with the bivariate association low β coefficient / Wald statistic), a second regression analysis with a stepwise selection procedure was run to identify the best independently contributing predictors.

Significant differences were identified in the bivariate and multivariable analyses employing factor loadings, regression analysis p values, and, where applicable, comparisons of 95 % confidence intervals (z set to 1.96).

Weighted bivariate and multivariate logistic analyses were used to assess the relationship between maternal depressive symptoms (trichotomized to depression at both time points, at 1 time point, and at neither time point) and parental prevention practices, while controlling for a wide variety of sociodemographic variables.

Potential collinearity of the independent variables was examined by comparing bivariate and multivariable analyses.

Finally, because the moderator variables may not be independent from each other, we checked whether the observed bivariate moderator effects would persist in multivariate analysis.

For each analysis, bivariate associations were calculated as tetrachoric correlations.

As a result, and together with incomplete data in some variables across subjects, the sample sizes for variables in the bivariate and multivariable analyses were less than those for the completed number of cross-sectional and registration - linked ACE score profiles.

Sample size differs across bivariate and multivariate analyses because of missing data.

Bivariate and multivariate analyses tested the study hypotheses.

Finally, the analysis of the remaining bivariate correlations allows for the following considerations.